Energy_Consumption
Luis Varela
7 01 2020
Introduction
There are a series of Submeters that meassure the energy consumption in different parts of an appartment. These meassurements will be used to forecast consumption on the next year, but above all there will be an exploration that will be made to give recommendations to the tenant of the building on how to save energy and why these devices can help reduce the energy consumption.
Used Libraries
library(RMySQL) #for the SQL queries
library(DBI) #necessary for the RMySQL library
library(ggplot2) #graph library
library(plotly) #this one is better for the line graphs
library(knitr) #for the tables aes
library(magrittr) #magic %>%
library(dplyr) #data wrangling
library(lubridate) #convert date and time
library(grid) #formating the ggplot graphics
library(ggfortify) #ploting TS object
library(forecast) #for forecastingFetch Data and Select Variables
#Importing the data, create a database connection
con <- dbConnect(MySQL(),
user = 'deepAnalytics',
password = 'Sqltask1234!',
dbname = 'dataanalytics2018',
host = 'data-analytics-2018.cbrosir2cswx.us-east-1.rds.amazonaws.com')
#List the tables contained in the database
dbListTables(con)output
## [1] "iris" "yr_2006" "yr_2007" "yr_2008" "yr_2009" "yr_2010"#quick check of attributes listed in one of the lists, I kow beforehand they all, except one, contain the same attributes
dbListFields(con, 'yr_2006')output
## [1] "id" "Date" "Time"
## [4] "Global_active_power" "Global_reactive_power" "Global_intensity"
## [7] "Voltage" "Sub_metering_1" "Sub_metering_2"
## [10] "Sub_metering_3"#save all df locally with all the info in them
yr2006_local <- dbGetQuery(con, "SELECT * FROM yr_2006")
write.csv(yr2006_local, file = "Data/yr2006_local")
yr2007_local <- dbGetQuery(con, "SELECT * FROM yr_2007")
write.csv(yr2007_local, file = "Data/yr2007_local")
yr2008_local <- dbGetQuery(con, "SELECT * FROM yr_2008")
write.csv(yr2008_local, file = "Data/yr2008_local")
yr2009_local <- dbGetQuery(con, "SELECT * FROM yr_2009")
write.csv(yr2009_local, file = "Data/yr2009_local")
yr2010_local <- dbGetQuery(con, "SELECT * FROM yr_2010")
write.csv(yr2010_local, file = "Data/yr2010_local")
#for the analysis I need to implement, only 5 attributes are relevant, Time, Date, Global_active_power and the 3 Sub_metering
yr_2006 <- select(yr2006_local, Date, Time, Sub_metering_1, Sub_metering_2, Sub_metering_3, Global_active_power)
yr_2007 <- select(yr2007_local, Date, Time, Sub_metering_1, Sub_metering_2, Sub_metering_3, Global_active_power)
yr_2008 <- select(yr2008_local, Date, Time, Sub_metering_1, Sub_metering_2, Sub_metering_3, Global_active_power)
yr_2009 <- select(yr2009_local, Date, Time, Sub_metering_1, Sub_metering_2, Sub_metering_3, Global_active_power)
yr_2010 <- select(yr2010_local, Date, Time, Sub_metering_1, Sub_metering_2, Sub_metering_3, Global_active_power)#str() visualized as kable table
x_str <- function(data){
data.frame(
variable = names(data),
class = sapply(data, typeof),
first_values = sapply(data, function(x) paste0(head(x), collapse = ", ")),
row.names = NULL) %>%
kable()
}Exploration of the Data
Year 2006
x_str(yr_2006)| variable | class | first_values |
|---|---|---|
| Date | character | 2006-12-16, 2006-12-16, 2006-12-16, 2006-12-16, 2006-12-16, 2006-12-16 |
| Time | character | 17:24:00, 17:25:00, 17:26:00, 17:27:00, 17:28:00, 17:29:00 |
| Sub_metering_1 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_2 | double | 1, 1, 2, 1, 1, 2 |
| Sub_metering_3 | double | 17, 16, 17, 17, 17, 17 |
| Global_active_power | double | 4.216, 5.36, 5.374, 5.388, 3.666, 3.52 |
kable(summary(yr_2006))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power | |
|---|---|---|---|---|---|---|
| Length:21992 | Length:21992 | Min. : 0.000 | Min. : 0.000 | Min. : 0.00 | Min. :0.194 | |
| Class :character | Class :character | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.00 | 1st Qu.:0.496 | |
| Mode :character | Mode :character | Median : 0.000 | Median : 0.000 | Median : 0.00 | Median :1.708 | |
| NA | NA | Mean : 1.249 | Mean : 2.215 | Mean : 7.41 | Mean :1.901 | |
| NA | NA | 3rd Qu.: 0.000 | 3rd Qu.: 1.000 | 3rd Qu.:17.00 | 3rd Qu.:2.692 | |
| NA | NA | Max. :77.000 | Max. :74.000 | Max. :20.00 | Max. :9.132 |
kable(head(yr_2006))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power |
|---|---|---|---|---|---|
| 2006-12-16 | 17:24:00 | 0 | 1 | 17 | 4.216 |
| 2006-12-16 | 17:25:00 | 0 | 1 | 16 | 5.360 |
| 2006-12-16 | 17:26:00 | 0 | 2 | 17 | 5.374 |
| 2006-12-16 | 17:27:00 | 0 | 1 | 17 | 5.388 |
| 2006-12-16 | 17:28:00 | 0 | 1 | 17 | 3.666 |
| 2006-12-16 | 17:29:00 | 0 | 2 | 17 | 3.520 |
Year 2007
x_str(yr_2007)| variable | class | first_values |
|---|---|---|
| Date | character | 2007-01-01, 2007-01-01, 2007-01-01, 2007-01-01, 2007-01-01, 2007-01-01 |
| Time | character | 00:00:00, 00:01:00, 00:02:00, 00:03:00, 00:04:00, 00:05:00 |
| Sub_metering_1 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_2 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_3 | double | 0, 0, 0, 0, 0, 0 |
| Global_active_power | double | 2.58, 2.552, 2.55, 2.55, 2.554, 2.55 |
kable(summary(yr_2007))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power | |
|---|---|---|---|---|---|---|
| Length:521669 | Length:521669 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.082 | |
| Class :character | Class :character | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.278 | |
| Mode :character | Mode :character | Median : 0.000 | Median : 0.000 | Median : 0.000 | Median : 0.504 | |
| NA | NA | Mean : 1.232 | Mean : 1.638 | Mean : 5.795 | Mean : 1.117 | |
| NA | NA | 3rd Qu.: 0.000 | 3rd Qu.: 1.000 | 3rd Qu.:17.000 | 3rd Qu.: 1.548 | |
| NA | NA | Max. :78.000 | Max. :78.000 | Max. :20.000 | Max. :10.670 |
kable(head(yr_2007))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power |
|---|---|---|---|---|---|
| 2007-01-01 | 00:00:00 | 0 | 0 | 0 | 2.580 |
| 2007-01-01 | 00:01:00 | 0 | 0 | 0 | 2.552 |
| 2007-01-01 | 00:02:00 | 0 | 0 | 0 | 2.550 |
| 2007-01-01 | 00:03:00 | 0 | 0 | 0 | 2.550 |
| 2007-01-01 | 00:04:00 | 0 | 0 | 0 | 2.554 |
| 2007-01-01 | 00:05:00 | 0 | 0 | 0 | 2.550 |
Year 2008
x_str(yr_2008)| variable | class | first_values |
|---|---|---|
| Date | character | 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01 |
| Time | character | 00:00:00, 00:01:00, 00:02:00, 00:03:00, 00:04:00, 00:05:00 |
| Sub_metering_1 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_2 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_3 | double | 18, 18, 18, 18, 18, 17 |
| Global_active_power | double | 1.62, 1.626, 1.622, 1.612, 1.612, 1.546 |
kable(summary(yr_2008))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power | |
|---|---|---|---|---|---|---|
| Length:526905 | Length:526905 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.076 | |
| Class :character | Class :character | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.300 | |
| Mode :character | Mode :character | Median : 0.00 | Median : 0.000 | Median : 1.000 | Median : 0.566 | |
| NA | NA | Mean : 1.11 | Mean : 1.256 | Mean : 6.034 | Mean : 1.072 | |
| NA | NA | 3rd Qu.: 0.00 | 3rd Qu.: 1.000 | 3rd Qu.:17.000 | 3rd Qu.: 1.518 | |
| NA | NA | Max. :80.00 | Max. :76.000 | Max. :31.000 | Max. :10.348 |
kable(head(yr_2008))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power |
|---|---|---|---|---|---|
| 2008-01-01 | 00:00:00 | 0 | 0 | 18 | 1.620 |
| 2008-01-01 | 00:01:00 | 0 | 0 | 18 | 1.626 |
| 2008-01-01 | 00:02:00 | 0 | 0 | 18 | 1.622 |
| 2008-01-01 | 00:03:00 | 0 | 0 | 18 | 1.612 |
| 2008-01-01 | 00:04:00 | 0 | 0 | 18 | 1.612 |
| 2008-01-01 | 00:05:00 | 0 | 0 | 17 | 1.546 |
Year 2009
x_str(yr_2009)| variable | class | first_values |
|---|---|---|
| Date | character | 2009-01-01, 2009-01-01, 2009-01-01, 2009-01-01, 2009-01-01, 2009-01-01 |
| Time | character | 00:00:00, 00:01:00, 00:02:00, 00:03:00, 00:04:00, 00:05:00 |
| Sub_metering_1 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_2 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_3 | double | 0, 0, 0, 0, 0, 0 |
| Global_active_power | double | 0.484, 0.484, 0.482, 0.482, 0.482, 0.57 |
kable(summary(yr_2009))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power | |
|---|---|---|---|---|---|---|
| Length:521320 | Length:521320 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.122 | |
| Class :character | Class :character | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.318 | |
| Mode :character | Mode :character | Median : 0.000 | Median : 0.000 | Median : 1.000 | Median : 0.622 | |
| NA | NA | Mean : 1.137 | Mean : 1.136 | Mean : 6.823 | Mean : 1.079 | |
| NA | NA | 3rd Qu.: 0.000 | 3rd Qu.: 1.000 | 3rd Qu.:18.000 | 3rd Qu.: 1.514 | |
| NA | NA | Max. :82.000 | Max. :77.000 | Max. :31.000 | Max. :11.122 |
kable(head(yr_2009))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power |
|---|---|---|---|---|---|
| 2009-01-01 | 00:00:00 | 0 | 0 | 0 | 0.484 |
| 2009-01-01 | 00:01:00 | 0 | 0 | 0 | 0.484 |
| 2009-01-01 | 00:02:00 | 0 | 0 | 0 | 0.482 |
| 2009-01-01 | 00:03:00 | 0 | 0 | 0 | 0.482 |
| 2009-01-01 | 00:04:00 | 0 | 0 | 0 | 0.482 |
| 2009-01-01 | 00:05:00 | 0 | 0 | 0 | 0.570 |
Year 2010
x_str(yr_2010)| variable | class | first_values |
|---|---|---|
| Date | character | 2010-01-01, 2010-01-01, 2010-01-01, 2010-01-01, 2010-01-01, 2010-01-01 |
| Time | character | 00:00:00, 00:01:00, 00:02:00, 00:03:00, 00:04:00, 00:05:00 |
| Sub_metering_1 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_2 | double | 0, 0, 0, 0, 0, 0 |
| Sub_metering_3 | double | 18, 18, 19, 18, 18, 19 |
| Global_active_power | double | 1.79, 1.78, 1.78, 1.746, 1.686, 1.686 |
kable(summary(yr_2010))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power | |
|---|---|---|---|---|---|---|
| Length:457394 | Length:457394 | Min. : 0.0000 | Min. : 0.000 | Min. : 0.000 | Min. :0.138 | |
| Class :character | Class :character | 1st Qu.: 0.0000 | 1st Qu.: 0.000 | 1st Qu.: 1.000 | 1st Qu.:0.336 | |
| Mode :character | Mode :character | Median : 0.0000 | Median : 0.000 | Median : 1.000 | Median :0.700 | |
| NA | NA | Mean : 0.9875 | Mean : 1.102 | Mean : 7.244 | Mean :1.061 | |
| NA | NA | 3rd Qu.: 0.0000 | 3rd Qu.: 1.000 | 3rd Qu.:18.000 | 3rd Qu.:1.512 | |
| NA | NA | Max. :88.0000 | Max. :80.000 | Max. :31.000 | Max. :9.724 |
kable(head(yr_2010))| Date | Time | Sub_metering_1 | Sub_metering_2 | Sub_metering_3 | Global_active_power |
|---|---|---|---|---|---|
| 2010-01-01 | 00:00:00 | 0 | 0 | 18 | 1.790 |
| 2010-01-01 | 00:01:00 | 0 | 0 | 18 | 1.780 |
| 2010-01-01 | 00:02:00 | 0 | 0 | 19 | 1.780 |
| 2010-01-01 | 00:03:00 | 0 | 0 | 18 | 1.746 |
| 2010-01-01 | 00:04:00 | 0 | 0 | 18 | 1.686 |
| 2010-01-01 | 00:05:00 | 0 | 0 | 19 | 1.686 |
Data Manipulation
#join the df that are complete and will be used for the analysis
yr_07_08_09 <- bind_rows(yr_2007, yr_2008, yr_2009)
#change the names of the columns
names(yr_07_08_09)[names(yr_07_08_09) == "Sub_metering_1"] <- "Kitchen"
names(yr_07_08_09)[names(yr_07_08_09) == "Sub_metering_2"] <- "Laundry_room"
names(yr_07_08_09)[names(yr_07_08_09) == "Sub_metering_3"] <- "Water_heater"
#changing the submeters to kw/h
yr_07_08_09$Kitchen <- yr_07_08_09$Kitchen/1000
yr_07_08_09$Laundry_room <- yr_07_08_09$Laundry_room/1000
yr_07_08_09$Water_heater <- yr_07_08_09$Water_heater/1000
kable(head(yr_07_08_09, n = 5))| Date | Time | Kitchen | Laundry_room | Water_heater | Global_active_power |
|---|---|---|---|---|---|
| 2007-01-01 | 00:00:00 | 0 | 0 | 0 | 2.580 |
| 2007-01-01 | 00:01:00 | 0 | 0 | 0 | 2.552 |
| 2007-01-01 | 00:02:00 | 0 | 0 | 0 | 2.550 |
| 2007-01-01 | 00:03:00 | 0 | 0 | 0 | 2.550 |
| 2007-01-01 | 00:04:00 | 0 | 0 | 0 | 2.554 |
#change the reading to the same unit in the "Global_active_power" to kw/h, and take from the reading the sum of the other three sub meters
yr_07_08_09$Rest_house <- round(yr_07_08_09$Global_active_power/60 - (yr_07_08_09$Kitchen + yr_07_08_09$Laundry_room + yr_07_08_09$Water_heater), 3)
kable(head(yr_07_08_09))| Date | Time | Kitchen | Laundry_room | Water_heater | Global_active_power | Rest_house |
|---|---|---|---|---|---|---|
| 2007-01-01 | 00:00:00 | 0 | 0 | 0 | 2.580 | 0.043 |
| 2007-01-01 | 00:01:00 | 0 | 0 | 0 | 2.552 | 0.043 |
| 2007-01-01 | 00:02:00 | 0 | 0 | 0 | 2.550 | 0.042 |
| 2007-01-01 | 00:03:00 | 0 | 0 | 0 | 2.550 | 0.042 |
| 2007-01-01 | 00:04:00 | 0 | 0 | 0 | 2.554 | 0.043 |
| 2007-01-01 | 00:05:00 | 0 | 0 | 0 | 2.550 | 0.042 |
#combine date and time attributes in a new column
yr_07_08_09$Date_time <- paste(yr_07_08_09$Date, yr_07_08_09$Time)
#moving the column
yr_07_08_09 <- yr_07_08_09[,c(ncol(yr_07_08_09), 1:(ncol(yr_07_08_09)-1))]Creation of New Variables Based on Date_time
#convert Date_time to POSIXct
yr_07_08_09$Date_time <- ymd_hms(yr_07_08_09$Date_time)
yr_07_08_09$Time <- hms(yr_07_08_09$Time)
yr_07_08_09$Date <- ymd(yr_07_08_09$Date)
#add the time zone
attr(yr_07_08_09$Date_time, "tzone") <- "Europe/Paris"
## Inspect the data types
x_str(yr_07_08_09)| variable | class | first_values |
|---|---|---|
| Date_time | double | 2007-01-01 01:00:00, 2007-01-01 01:01:00, 2007-01-01 01:02:00, 2007-01-01 01:03:00, 2007-01-01 01:04:00, 2007-01-01 01:05:00 |
| Date | double | 2007-01-01, 2007-01-01, 2007-01-01, 2007-01-01, 2007-01-01, 2007-01-01 |
| Time | double | 0S, 1M 0S, 2M 0S, 3M 0S, 4M 0S, 5M 0S |
| Kitchen | double | 0, 0, 0, 0, 0, 0 |
| Laundry_room | double | 0, 0, 0, 0, 0, 0 |
| Water_heater | double | 0, 0, 0, 0, 0, 0 |
| Global_active_power | double | 2.58, 2.552, 2.55, 2.55, 2.554, 2.55 |
| Rest_house | double | 0.043, 0.043, 0.042, 0.042, 0.043, 0.042 |
#create new columns for information mining
yr_07_08_09$Year <- year(yr_07_08_09$Date_time)
yr_07_08_09$Quarter <- quarter(yr_07_08_09$Date_time)
yr_07_08_09$Weekday <- wday(yr_07_08_09$Date_time)
yr_07_08_09$Minute <- minute(yr_07_08_09$Date_time)
yr_07_08_09$Month <- month(yr_07_08_09$Date_time)
yr_07_08_09$Day <- day(yr_07_08_09$Date_time)
yr_07_08_09$Hour <- hour(yr_07_08_09$Date_time)#checking how much energy each submeter used in 2008 during the weekend and workdays
submeters_consumption_08 <- yr_07_08_09 %>%
filter(Year == 2008, Weekday == 1 & 2 & 3 & 4 & 5) %>%
select(Kitchen, Laundry_room, Water_heater, Rest_house) %>%
summarise_all(funs(sum)) %>%
tidyr::gather(key = Area, value = KwH) %>%
mutate(Porcentage = round(KwH/sum(KwH) * 100, 2))
#pie chart showing the porcentage of power used by each submeter in 2008
ggplot(submeters_consumption_08, aes(x = "", y = KwH, fill = Area)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
scale_fill_brewer(palette = "Dark2") +
blank_theme + #Previously created object, refer to RMD doc to see it
theme(axis.text.x = element_blank()) +
# theme(plot.margin = unit(c(0, -10, -10, -30), "mm")) +
geom_text(aes(label = Porcentage), position = position_stack(vjust = 0.5)) +
ggtitle("Sum of the Energy Consumption During all Weekdays in 2008")
#checking how much energy each submeter used during the sum of the weekends of the year
submeters_consumption_weekends <- yr_07_08_09 %>%
filter(Weekday == 6 & 7) %>%
select(Kitchen, Laundry_room, Water_heater, Rest_house) %>%
summarise_all(funs(sum)) %>%
tidyr::gather(key = Area, value = KwH) %>%
mutate(Porcentage = round(KwH/sum(KwH) * 100, 2))
#pie chart showing the porcentage of power used by each submeter in 2008
ggplot(submeters_consumption_weekends, aes(x = "", y = KwH, fill = Area)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
scale_fill_brewer(palette = "Dark2") +
blank_theme + #Previously created object, refer to RMD doc to see it
theme(axis.text.x = element_blank()) +
# theme(plot.margin = unit(c(0, -10, -10, -30), "mm")) +
geom_text(aes(label = Porcentage), position = position_stack(vjust = 0.5)) +
ggtitle("Sum of the Energy Consumption During All Weekends in 2008")
#Note to self: quite the jump in hot water ussage during the weekends. People are more at home, that´d explain it.
#overall ussage of energy is 6 times higher on the weekends than on the weekdays
Inspection Patterns of Consumption in Time
DAY
#plot of the mid summer week, friday 2009
Fri_summer <- yr_07_08_09 %>%
filter(Year == 2009 & Month == 6 & Day == 26 & (Minute == 0 | Minute == 10 | Minute == 20 | Minute == 30 | Minute == 40 | Minute == 50))
plot_ly(Fri_summer, x = ~Date_time, y = ~Kitchen, name = 'Kitchen', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~Laundry_room, mode = 'lines') %>%
add_trace(y = ~Water_heater, mode = 'lines') %>%
add_trace(y = ~Rest_house, mode = 'lines') %>%
layout(title = "Energy Consumption Mid-Summer Friday 26th of June 2009",
xaxis = list(title = "Time"),
yaxis = list (title = "KwH"))WEEK
#plot of the mid summer week of 2009
Fri_summer <- yr_07_08_09 %>%
filter(Date_time > "2009-06-22" & Date_time < "2009-06-28" & (Hour == 1 | Hour == 2 | Hour == 3 | Hour == 4 | Hour == 5 | Hour == 6 | Hour == 7 | Hour == 8 | Hour == 9 | Hour == 10 | Hour == 11 | Hour == 12 | Hour == 13 | Hour == 14 | Hour == 15 | Hour == 16 | Hour == 17 | Hour == 18 | Hour == 19 | Hour == 20 | Hour == 21 |Hour == 22 | Hour == 23 | Hour == 0))
plot_ly(Fri_summer, x = ~Date_time, y = ~Kitchen, name = 'Kitchen', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~Laundry_room, mode = 'lines') %>%
add_trace(y = ~Water_heater, mode = 'lines') %>%
add_trace(y = ~Rest_house, mode = 'lines') %>%
layout(title = "Energy Consumption Mid-Summer Week 2009",
xaxis = list(title = "Time"),
yaxis = list (title = "KwH"))MONTH
#plot of the mid summer week of 2009
Jul_2009 <- yr_07_08_09 %>%
filter(Year == 2009 & Month == 7 & (Hour == 1 | Hour == 2 | Hour == 3 | Hour == 4 | Hour == 5 | Hour == 6 | Hour == 7 | Hour == 8 | Hour == 9 | Hour == 10 | Hour == 11 | Hour == 12 | Hour == 13 | Hour == 14 | Hour == 15 | Hour == 16 | Hour == 17 | Hour == 18 | Hour == 19 | Hour == 20 | Hour == 21 |Hour == 22 | Hour == 23 | Hour == 0))
plot_ly(Jul_2009, x = ~Date_time, y = ~Kitchen, name = 'Kitchen', type = 'scatter', mode = 'lines') %>%
add_trace(y = ~Laundry_room, mode = 'lines') %>%
add_trace(y = ~Water_heater, mode = 'lines') %>%
add_trace(y = ~Rest_house, mode = 'lines') %>%
layout(title = "Energy Consumption June 2009",
xaxis = list(title = "Time"),
yaxis = list (title = "KwH"))Forecasting on Each Submeter
KITCHEN
#subset to one observation per day on
weekly_07_09 <- filter(yr_07_08_09, Weekday == 1 &
Hour == 20 & Minute == 1)
#TS object with submeter from Kitchen
tsKitchen <- ts(weekly_07_09$Kitchen, frequency=52, start = c(2007, 1))
#ploting submeter Kitchen with autoplot - add labels, color
autoplot(tsKitchen, ts.colour = 'red', xlab = "Time", ylab = "KwH", main = "Kitchen")
#trying other plotting package
plot.ts(tsKitchen)
#linear model
fitKitchen <- tslm(tsKitchen ~ trend + season)
summary(fitKitchen)output
##
## Call:
## tslm(formula = tsKitchen ~ trend + season)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.026024 -0.001357 0.000000 0.001357 0.027357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.716e-03 5.816e-03 0.295 0.76855
## trend -2.609e-05 1.913e-05 -1.364 0.17553
## season2 2.609e-05 8.100e-03 0.003 0.99744
## season3 1.172e-02 8.100e-03 1.447 0.15107
## season4 2.441e-02 8.100e-03 3.014 0.00327 **
## season5 1.044e-04 8.100e-03 0.013 0.98975
## season6 1.305e-04 8.100e-03 0.016 0.98718
## season7 4.899e-04 8.100e-03 0.060 0.95190
## season8 -1.507e-04 8.101e-03 -0.019 0.98520
## season9 1.875e-03 8.101e-03 0.232 0.81740
## season10 1.257e-02 8.101e-03 1.551 0.12398
## season11 1.226e-02 8.102e-03 1.513 0.13335
## season12 -4.632e-05 8.102e-03 -0.006 0.99545
## season13 1.298e-02 8.103e-03 1.602 0.11234
## season14 5.863e-06 8.103e-03 0.001 0.99942
## season15 3.196e-05 8.104e-03 0.004 0.99686
## season16 5.805e-05 8.105e-03 0.007 0.99430
## season17 8.414e-05 8.105e-03 0.010 0.99174
## season18 4.436e-04 8.106e-03 0.055 0.95647
## season19 1.363e-04 8.107e-03 0.017 0.98662
## season20 4.957e-04 8.108e-03 0.061 0.95137
## season21 1.885e-04 8.109e-03 0.023 0.98150
## season22 5.479e-04 8.110e-03 0.068 0.94627
## season23 1.257e-02 8.111e-03 1.550 0.12422
## season24 2.668e-04 8.112e-03 0.033 0.97383
## season25 6.262e-04 8.113e-03 0.077 0.93863
## season26 3.190e-04 8.114e-03 0.039 0.96872
## season27 3.451e-04 8.115e-03 0.043 0.96617
## season28 3.712e-04 8.116e-03 0.046 0.96362
## season29 1.340e-02 8.117e-03 1.650 0.10199
## season30 4.233e-04 8.119e-03 0.052 0.95852
## season31 4.494e-04 8.120e-03 0.055 0.95597
## season32 4.755e-04 8.121e-03 0.059 0.95343
## season33 5.016e-04 8.123e-03 0.062 0.95088
## season34 5.277e-04 8.124e-03 0.065 0.94834
## season35 5.538e-04 8.126e-03 0.068 0.94580
## season36 5.799e-04 8.127e-03 0.071 0.94326
## season37 6.060e-04 8.129e-03 0.075 0.94072
## season38 6.321e-04 8.130e-03 0.078 0.93819
## season39 6.582e-04 8.132e-03 0.081 0.93566
## season40 6.843e-04 8.134e-03 0.084 0.93313
## season41 7.103e-04 8.136e-03 0.087 0.93060
## season42 7.364e-04 8.137e-03 0.090 0.92807
## season43 2.376e-02 8.139e-03 2.919 0.00433 **
## season44 1.112e-02 8.141e-03 1.366 0.17496
## season45 1.381e-02 8.143e-03 1.696 0.09291 .
## season46 1.351e-02 8.145e-03 1.658 0.10038
## season47 8.669e-04 8.147e-03 0.106 0.91547
## season48 8.930e-04 8.149e-03 0.110 0.91296
## season49 1.252e-03 8.151e-03 0.154 0.87820
## season50 7.668e-04 9.066e-03 0.085 0.93277
## season51 2.929e-04 9.067e-03 0.032 0.97430
## season52 7.819e-03 9.068e-03 0.862 0.39062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00992 on 100 degrees of freedom
## Multiple R-squared: 0.3855, Adjusted R-squared: 0.06597
## F-statistic: 1.206 on 52 and 100 DF, p-value: 0.21#create the forecast for submeter in the Kitchen. Forecast ahead 20 time periods
forecastfitKitchen <- forecast(fitKitchen, h=20)
plot(forecastfitKitchen)
#create submeter Kitchen forecast with confidence levels 80 and 90
forecastfitKitchen2 <- forecast(fitKitchen, h=20, level = c(80, 90))
plot(forecastfitKitchen2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time")
#ecompose submeter Kitchen into trend, seasonal and remainder
decomposedForecast <- decompose(tsKitchen)
#plot decomposed sub-meter 1
plot(decomposedForecast, col = "red")
#statistic summary
summary(decomposedForecast)output
## Length Class Mode
## x 153 ts numeric
## seasonal 153 ts numeric
## trend 153 ts numeric
## random 153 ts numeric
## figure 52 -none- numeric
## type 1 -none- character#seasonal adjusting sub-meter 1 by subtracting the seasonal component & plot
tsAdjusted <- tsKitchen - decomposedForecast$seasonal
autoplot(tsAdjusted, col = 'red', xlab = "Time", ylab = "KwH", main = "Kitchen")
#now usging Holt Winters Exponential Smoothing
tsHW <- HoltWinters(tsAdjusted, beta = FALSE, gamma = FALSE)
plot(tsHW, ylim = c(-0.035, 0.045))
#forecast Holt Winters
tsHWforecast <- forecast(tsHW, h=25)
plot(tsHWforecast, ylim = c(0, 0.04), ylab= "KwH", xlab = "Time - Kitchen")
#forecast HoltWinters with diminished confidence levels
tsHWforecast2 <- forecast(tsHW, h = 25, level = c(10, 25))
plot(tsHWforecast2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time - Kitchen", start(2010))
LAUNDRY-ROOM
#subset to one observation per day on
weekly_07_09_laundry <- filter(yr_07_08_09, Weekday == 1 &
Hour == 20 & Minute == 1)
#TS object with submeter from Kitchen
tsLaundry <- ts(weekly_07_09_laundry$Laundry_room, frequency=52, start = c(2007, 1))
#ploting submeter Kitchen with autoplot - add labels, color
autoplot(tsLaundry, ts.colour = 'red', xlab = "Time", ylab = "KwH", main = "Laundry_room")
#trying other plotting package
plot.ts(tsLaundry)
#linear model
fitLaundry <- tslm(tsLaundry ~ trend + season)
summary(fitLaundry)output
##
## Call:
## tslm(formula = tsLaundry ~ trend + season)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.015298 -0.001965 0.000000 0.001333 0.025667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.336e-03 4.451e-03 0.525 0.6009
## trend -3.778e-05 1.464e-05 -2.582 0.0113 *
## season2 -2.955e-04 6.198e-03 -0.048 0.9621
## season3 -2.578e-04 6.198e-03 -0.042 0.9669
## season4 7.800e-04 6.199e-03 0.126 0.9001
## season5 1.215e-02 6.199e-03 1.960 0.0527 .
## season6 5.223e-04 6.199e-03 0.084 0.9330
## season7 2.267e-04 6.199e-03 0.037 0.9709
## season8 5.978e-04 6.199e-03 0.096 0.9234
## season9 2.969e-03 6.200e-03 0.479 0.6331
## season10 6.732e-06 6.200e-03 0.001 0.9991
## season11 4.452e-05 6.200e-03 0.007 0.9943
## season12 7.490e-04 6.201e-03 0.121 0.9041
## season13 1.079e-02 6.201e-03 1.740 0.0850 .
## season14 1.491e-03 6.201e-03 0.240 0.8105
## season15 5.290e-04 6.202e-03 0.085 0.9322
## season16 5.668e-04 6.202e-03 0.091 0.9274
## season17 1.271e-03 6.203e-03 0.205 0.8380
## season18 6.423e-04 6.203e-03 0.104 0.9177
## season19 1.680e-03 6.204e-03 0.271 0.7871
## season20 1.005e-02 6.205e-03 1.620 0.1084
## season21 7.557e-04 6.205e-03 0.122 0.9033
## season22 1.379e-02 6.206e-03 2.223 0.0285 *
## season23 1.216e-02 6.207e-03 1.960 0.0528 .
## season24 8.691e-04 6.208e-03 0.140 0.8889
## season25 9.068e-04 6.208e-03 0.146 0.8842
## season26 1.028e-02 6.209e-03 1.655 0.1010
## season27 9.824e-04 6.210e-03 0.158 0.8746
## season28 1.020e-03 6.211e-03 0.164 0.8699
## season29 7.246e-04 6.212e-03 0.117 0.9074
## season30 1.096e-03 6.213e-03 0.176 0.8604
## season31 9.800e-03 6.214e-03 1.577 0.1179
## season32 1.171e-03 6.215e-03 0.188 0.8509
## season33 2.209e-03 6.216e-03 0.355 0.7230
## season34 1.580e-03 6.217e-03 0.254 0.7999
## season35 2.285e-03 6.218e-03 0.367 0.7141
## season36 1.656e-03 6.220e-03 0.266 0.7906
## season37 1.027e-03 6.221e-03 0.165 0.8692
## season38 1.731e-03 6.222e-03 0.278 0.7814
## season39 1.769e-03 6.223e-03 0.284 0.7768
## season40 1.140e-03 6.225e-03 0.183 0.8550
## season41 1.845e-03 6.226e-03 0.296 0.7676
## season42 2.883e-03 6.227e-03 0.463 0.6445
## season43 8.587e-03 6.229e-03 1.379 0.1711
## season44 2.291e-03 6.230e-03 0.368 0.7138
## season45 7.329e-03 6.232e-03 1.176 0.2423
## season46 2.367e-03 6.233e-03 0.380 0.7049
## season47 1.407e-02 6.235e-03 2.257 0.0262 *
## season48 1.776e-03 6.236e-03 0.285 0.7764
## season49 5.480e-03 6.238e-03 0.879 0.3818
## season50 5.357e-04 6.938e-03 0.077 0.9386
## season51 5.735e-04 6.939e-03 0.083 0.9343
## season52 2.111e-03 6.940e-03 0.304 0.7616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.007591 on 100 degrees of freedom
## Multiple R-squared: 0.3273, Adjusted R-squared: -0.02244
## F-statistic: 0.9358 on 52 and 100 DF, p-value: 0.5975#create the forecast for submeter in the Kitchen. Forecast ahead 20 time periods
forecastfitLaundry <- forecast(fitLaundry, h=20)
plot(forecastfitKitchen)
#create submeter Kitchen forecast with confidence levels 80 and 90
forecastfitLaundry2 <- forecast(fitLaundry, h=20, level = c(80, 90))
plot(forecastfitLaundry2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time")
#ecompose submeter Kitchen into trend, seasonal and remainder
decomposedForecastLaundry <- decompose(tsLaundry)
#plot decomposed sub-meter 1
plot(decomposedForecastLaundry, col = "red")
#statistic summary
summary(decomposedForecastLaundry)output
## Length Class Mode
## x 153 ts numeric
## seasonal 153 ts numeric
## trend 153 ts numeric
## random 153 ts numeric
## figure 52 -none- numeric
## type 1 -none- character#seasonal adjusting sub-meter 1 by subtracting the seasonal component & plot
tsAdjustedLaundry <- tsLaundry - decomposedForecastLaundry$seasonal
autoplot(tsAdjustedLaundry, col = 'red', xlab = "Time", ylab = "KwH", main = "Laundry_Room")
#now usging Holt Winters Exponential Smoothing
tsHWLaundry <- HoltWinters(tsAdjustedLaundry, beta = FALSE, gamma = FALSE)
plot(tsHW, ylim = c(-0.035, 0.045))
#forecast Holt Winters
tsHWforecastLaundry <- forecast(tsHWLaundry, h=25)
plot(tsHWforecast, ylim = c(0, 0.04), ylab= "KwH", xlab = "Time - Kitchen")
#forecast HoltWinters with diminished confidence levels
tsHWforecastLaundry2 <- forecast(tsHWLaundry, h = 25, level = c(10, 25))
plot(tsHWforecastLaundry2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time - Laundry_Room", start(2010))
WATER HEATER
#subset to one observation per day on
weekly_07_09_WaterH <- filter(yr_07_08_09, Weekday == 1 &
Hour == 20 & Minute == 1)
#TS object with submeter from Kitchen
tsWaterH <- ts(weekly_07_09_WaterH$Water_heater, frequency=52, start = c(2007, 1))
#ploting submeter Kitchen with autoplot - add labels, color
autoplot(tsWaterH, ts.colour = 'red', xlab = "Time", ylab = "KwH", main = "Water_heater and AC")
#trying other plotting package
plot.ts(tsWaterH)
#linear model
fitWaterH <- tslm(tsWaterH ~ trend + season)
summary(fitWaterH)output
##
## Call:
## tslm(formula = tsWaterH ~ trend + season)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0127169 -0.0052831 -0.0003836 0.0052831 0.0127169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.930e-03 4.695e-03 2.115 0.0369 *
## trend 2.020e-05 1.544e-05 1.308 0.1937
## season2 3.131e-04 6.537e-03 0.048 0.9619
## season3 -5.707e-03 6.538e-03 -0.873 0.3848
## season4 5.606e-03 6.538e-03 0.858 0.3932
## season5 -5.747e-03 6.538e-03 -0.879 0.3814
## season6 5.566e-03 6.538e-03 0.851 0.3966
## season7 -1.112e-02 6.538e-03 -1.701 0.0921 .
## season8 -1.114e-02 6.538e-03 -1.704 0.0915 .
## season9 1.718e-04 6.539e-03 0.026 0.9791
## season10 6.485e-03 6.539e-03 0.992 0.3237
## season11 3.131e-03 6.539e-03 0.479 0.6331
## season12 -5.222e-03 6.540e-03 -0.799 0.4265
## season13 -5.242e-03 6.540e-03 -0.802 0.4247
## season14 -1.060e-02 6.541e-03 -1.620 0.1084
## season15 -1.062e-02 6.541e-03 -1.623 0.1077
## season16 -7.636e-03 6.542e-03 -1.167 0.2458
## season17 -8.990e-03 6.542e-03 -1.374 0.1725
## season18 -1.068e-02 6.543e-03 -1.632 0.1059
## season19 -4.697e-03 6.543e-03 -0.718 0.4745
## season20 -5.384e-03 6.544e-03 -0.823 0.4126
## season21 -7.061e-05 6.545e-03 -0.011 0.9914
## season22 -5.091e-03 6.545e-03 -0.778 0.4385
## season23 9.889e-03 6.546e-03 1.511 0.1340
## season24 -5.131e-03 6.547e-03 -0.784 0.4350
## season25 -5.151e-03 6.548e-03 -0.787 0.4333
## season26 -7.505e-03 6.549e-03 -1.146 0.2545
## season27 -1.119e-02 6.550e-03 -1.709 0.0906 .
## season28 -5.212e-03 6.551e-03 -0.796 0.4281
## season29 -1.123e-02 6.552e-03 -1.714 0.0896 .
## season30 -5.252e-03 6.553e-03 -0.802 0.4247
## season31 -5.273e-03 6.554e-03 -0.805 0.4230
## season32 -4.959e-03 6.555e-03 -0.757 0.4511
## season33 1.020e-03 6.556e-03 0.156 0.8766
## season34 -5.333e-03 6.557e-03 -0.813 0.4180
## season35 6.466e-04 6.558e-03 0.099 0.9217
## season36 -5.040e-03 6.560e-03 -0.768 0.4441
## season37 -9.060e-03 6.561e-03 -1.381 0.1704
## season38 -1.108e-02 6.562e-03 -1.689 0.0944 .
## season39 2.325e-04 6.564e-03 0.035 0.9718
## season40 -5.788e-03 6.565e-03 -0.882 0.3801
## season41 -1.181e-02 6.567e-03 -1.798 0.0752 .
## season42 -1.614e-04 6.568e-03 -0.025 0.9804
## season43 -5.150e-04 6.570e-03 -0.078 0.9377
## season44 -5.868e-03 6.571e-03 -0.893 0.3740
## season45 -5.889e-03 6.573e-03 -0.896 0.3724
## season46 -2.422e-04 6.574e-03 -0.037 0.9707
## season47 5.738e-03 6.576e-03 0.873 0.3850
## season48 -6.283e-03 6.578e-03 -0.955 0.3418
## season49 3.054e-05 6.579e-03 0.005 0.9963
## season50 -2.965e-03 7.318e-03 -0.405 0.6863
## season51 -1.148e-02 7.318e-03 -1.569 0.1197
## season52 5.495e-03 7.319e-03 0.751 0.4546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008007 on 100 degrees of freedom
## Multiple R-squared: 0.4152, Adjusted R-squared: 0.1111
## F-statistic: 1.365 on 52 and 100 DF, p-value: 0.09211#create the forecast for submeter in the Kitchen. Forecast ahead 20 time periods
forecastfitWaterH <- forecast(fitWaterH, h=20)
plot(forecastfitWaterH)
#create submeter Kitchen forecast with confidence levels 80 and 90
forecastfitWaterH2 <- forecast(fitWaterH, h=20, level = c(80, 90))
plot(forecastfitWaterH2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time")
#ecompose submeter Kitchen into trend, seasonal and remainder
decomposedForecastWaterH <- decompose(tsWaterH)
#plot decomposed sub-meter 1
plot(decomposedForecastWaterH, col = "red")
#statistic summary
summary(decomposedForecastWaterH)output
## Length Class Mode
## x 153 ts numeric
## seasonal 153 ts numeric
## trend 153 ts numeric
## random 153 ts numeric
## figure 52 -none- numeric
## type 1 -none- character#seasonal adjusting sub-meter 1 by subtracting the seasonal component & plot
tsAdjustedWaterH <- tsWaterH - decomposedForecastWaterH$seasonal
autoplot(tsAdjustedWaterH, col = 'red', xlab = "Time", ylab = "KwH", main = "Water_heater and AC")
#now usging Holt Winters Exponential Smoothing
tsHWWaterH <- HoltWinters(tsAdjustedWaterH, beta = FALSE, gamma = FALSE)
plot(tsWaterH, ylim = c(-0.035, 0.045))
#forecast Holt Winters
tsHWforecastWaterH <- forecast(tsHWWaterH, h=25)
plot(tsHWforecastWaterH, ylim = c(0, 0.04), ylab= "KwH", xlab = "Time - Water_heater and AC")
#forecast HoltWinters with diminished confidence levels
tsHWforecastWaterH2 <- forecast(tsHWWaterH, h = 25, level = c(10, 25))
plot(tsHWforecastWaterH2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time - Water_heater and AC", start(2010))
REST OF THE HOUSE
#subset to one observation per day on
weekly_07_09_House <- filter(yr_07_08_09, Weekday == 1 &
Hour == 20 & Minute == 1)
#TS object with submeter from Kitchen
tsHouse <- ts(weekly_07_09_House$Rest_house, frequency=52, start = c(2007, 1))
#ploting submeter Kitchen with autoplot - add labels, color
autoplot(tsHouse, ts.colour = 'red', xlab = "Time", ylab = "KwH", main = "Rest of the House")
#trying other plotting package
plot.ts(tsHouse)
#linear model
fitHouse <- tslm(tsHouse ~ trend + season)
summary(fitHouse)output
##
## Call:
## tslm(formula = tsHouse ~ trend + season)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0292864 -0.0043802 -0.0002864 0.0047136 0.0240000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.377e-02 6.582e-03 9.688 4.79e-16 ***
## trend -5.218e-05 2.164e-05 -2.411 0.017725 *
## season2 -2.595e-02 9.166e-03 -2.831 0.005612 **
## season3 -1.856e-02 9.166e-03 -2.025 0.045519 *
## season4 -1.951e-02 9.166e-03 -2.129 0.035752 *
## season5 -3.479e-02 9.166e-03 -3.796 0.000253 ***
## season6 -3.241e-02 9.167e-03 -3.535 0.000619 ***
## season7 -3.635e-02 9.167e-03 -3.966 0.000138 ***
## season8 -4.063e-02 9.167e-03 -4.433 2.39e-05 ***
## season9 -2.858e-02 9.168e-03 -3.118 0.002380 **
## season10 -3.320e-02 9.168e-03 -3.621 0.000463 ***
## season11 -3.948e-02 9.168e-03 -4.306 3.89e-05 ***
## season12 -5.243e-02 9.169e-03 -5.718 1.12e-07 ***
## season13 -4.437e-02 9.170e-03 -4.839 4.74e-06 ***
## season14 -5.599e-02 9.170e-03 -6.105 1.96e-08 ***
## season15 -5.527e-02 9.171e-03 -6.027 2.81e-08 ***
## season16 -4.755e-02 9.172e-03 -5.185 1.13e-06 ***
## season17 -4.650e-02 9.172e-03 -5.069 1.84e-06 ***
## season18 -5.078e-02 9.173e-03 -5.536 2.50e-07 ***
## season19 -4.973e-02 9.174e-03 -5.420 4.13e-07 ***
## season20 -4.834e-02 9.175e-03 -5.269 7.91e-07 ***
## season21 -4.896e-02 9.176e-03 -5.335 5.96e-07 ***
## season22 -5.557e-02 9.177e-03 -6.055 2.47e-08 ***
## season23 -4.785e-02 9.178e-03 -5.214 1.00e-06 ***
## season24 -5.480e-02 9.179e-03 -5.970 3.64e-08 ***
## season25 -5.441e-02 9.181e-03 -5.927 4.41e-08 ***
## season26 -5.403e-02 9.182e-03 -5.884 5.34e-08 ***
## season27 -5.598e-02 9.183e-03 -6.096 2.06e-08 ***
## season28 -5.592e-02 9.184e-03 -6.089 2.12e-08 ***
## season29 -5.287e-02 9.186e-03 -5.756 9.49e-08 ***
## season30 -5.249e-02 9.187e-03 -5.713 1.15e-07 ***
## season31 -5.177e-02 9.189e-03 -5.634 1.63e-07 ***
## season32 -5.538e-02 9.190e-03 -6.026 2.82e-08 ***
## season33 -5.200e-02 9.192e-03 -5.657 1.47e-07 ***
## season34 -5.294e-02 9.194e-03 -5.759 9.36e-08 ***
## season35 -4.756e-02 9.195e-03 -5.172 1.19e-06 ***
## season36 -5.184e-02 9.197e-03 -5.637 1.61e-07 ***
## season37 -5.079e-02 9.199e-03 -5.521 2.67e-07 ***
## season38 -5.340e-02 9.201e-03 -5.804 7.65e-08 ***
## season39 -5.068e-02 9.203e-03 -5.507 2.83e-07 ***
## season40 -4.130e-02 9.205e-03 -4.487 1.94e-05 ***
## season41 -4.791e-02 9.207e-03 -5.204 1.04e-06 ***
## season42 -3.053e-02 9.209e-03 -3.315 0.001277 **
## season43 -2.681e-02 9.211e-03 -2.911 0.004448 **
## season44 -2.276e-02 9.213e-03 -2.470 0.015204 *
## season45 -2.037e-02 9.215e-03 -2.211 0.029347 *
## season46 -1.432e-02 9.217e-03 -1.553 0.123488
## season47 -1.560e-02 9.220e-03 -1.692 0.093767 .
## season48 -3.288e-02 9.222e-03 -3.565 0.000559 ***
## season49 -2.016e-02 9.225e-03 -2.186 0.031174 *
## season50 -3.280e-02 1.026e-02 -3.197 0.001860 **
## season51 -1.475e-02 1.026e-02 -1.437 0.153767
## season52 -1.070e-02 1.026e-02 -1.042 0.299818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01123 on 100 degrees of freedom
## Multiple R-squared: 0.7265, Adjusted R-squared: 0.5843
## F-statistic: 5.109 on 52 and 100 DF, p-value: 1.528e-12#create the forecast for submeter in the Kitchen. Forecast ahead 20 time periods
forecastfitHouse <- forecast(fitHouse, h=20)
plot(forecastfitHouse)
#create submeter Kitchen forecast with confidence levels 80 and 90
forecastfitHouse2 <- forecast(fitHouse, h=20, level = c(80, 90))
plot(forecastfitHouse2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time")
#ecompose submeter Kitchen into trend, seasonal and remainder
decomposedForecastHouse <- decompose(tsHouse)
#plot decomposed sub-meter 1
plot(decomposedForecastHouse, col = "red")
#statistic summary
summary(decomposedForecastHouse)output
## Length Class Mode
## x 153 ts numeric
## seasonal 153 ts numeric
## trend 153 ts numeric
## random 153 ts numeric
## figure 52 -none- numeric
## type 1 -none- character#seasonal adjusting sub-meter 1 by subtracting the seasonal component & plot
tsAdjustedHouse <- tsHouse - decomposedForecastHouse$seasonal
autoplot(tsAdjustedHouse, col = 'red', xlab = "Time", ylab = "KwH", main = "Rest of the House")
#now usging Holt Winters Exponential Smoothing
tsHWHouse <- HoltWinters(tsAdjustedHouse, beta = FALSE, gamma = FALSE)
plot(tsHouse, ylim = c(-0.035, 0.045))
#forecast Holt Winters
tsHWforecastHouse <- forecast(tsHWHouse, h=25)
plot(tsHWforecastHouse, ylim = c(0, 0.04), ylab= "KwH", xlab = "Time - Rest of the House")
#forecast HoltWinters with diminished confidence levels
tsHWforecastHouse2 <- forecast(tsHWHouse, h = 25, level = c(10, 25))
plot(tsHWforecastHouse2, ylim = c(0, 0.04), ylab = "KwH", xlab = "Time - Rest of the House", start(2010))
#sesh info
sessionInfo()output
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
## [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
## [5] LC_TIME=German_Germany.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forecast_8.12 ggfortify_0.4.10 lubridate_1.7.8 dplyr_0.8.5
## [5] magrittr_1.5 knitr_1.28 plotly_4.9.2.1 ggplot2_3.3.0
## [9] RMySQL_0.10.20 DBI_1.1.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 lattice_0.20-41 tidyr_1.0.2 zoo_1.8-7
## [5] assertthat_0.2.1 digest_0.6.25 lmtest_0.9-37 R6_2.4.1
## [9] evaluate_0.14 httr_1.4.1 highr_0.8 pillar_1.4.4
## [13] rlang_0.4.6 lazyeval_0.2.2 curl_4.3 data.table_1.12.8
## [17] fracdiff_1.5-1 TTR_0.23-6 rmarkdown_2.1 labeling_0.3
## [21] stringr_1.4.0 htmlwidgets_1.5.1 munsell_0.5.0 compiler_4.0.0
## [25] xfun_0.13 pkgconfig_2.0.3 urca_1.3-0 htmltools_0.4.0
## [29] nnet_7.3-13 tidyselect_1.0.0 tibble_3.0.0 gridExtra_2.3
## [33] quadprog_1.5-8 fansi_0.4.1 viridisLite_0.3.0 crayon_1.3.4
## [37] withr_2.1.2 nlme_3.1-147 jsonlite_1.6.1 gtable_0.3.0
## [41] lifecycle_0.2.0 scales_1.1.1 quantmod_0.4.17 cli_2.0.2
## [45] stringi_1.4.6 farver_2.0.3 tseries_0.10-47 timeDate_3043.102
## [49] ellipsis_0.3.0 xts_0.12-0 generics_0.0.2 vctrs_0.2.4
## [53] RColorBrewer_1.1-2 tools_4.0.0 glue_1.4.0 purrr_0.3.4
## [57] crosstalk_1.1.0.1 parallel_4.0.0 yaml_2.2.1 colorspace_1.4-1